Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations1401
Missing cells12914
Missing cells (%)29.7%
Duplicate rows2
Duplicate rows (%)0.1%
Total size in memory339.4 KiB
Average record size in memory248.1 B

Variable types

Text7
DateTime2
Categorical11
Numeric8
Unsupported3

Alerts

Currency has constant value "USD" Constant
Fulfillment Status has constant value "Completed" Constant
Fulfillment Location has constant value "Belly Rubb" Constant
Recipient Region has constant value "CA" Constant
Dataset has 2 (0.1%) duplicate rowsDuplicates
Channels is highly overall correlated with Fulfillment Type and 8 other fieldsHigh correlation
Fulfillment Type is highly overall correlated with Channels and 5 other fieldsHigh correlation
Item Options Total Price is highly overall correlated with Channels and 3 other fieldsHigh correlation
Item Price is highly overall correlated with Channels and 3 other fieldsHigh correlation
Item Quantity is highly overall correlated with Recipient Address and 2 other fieldsHigh correlation
Item Total Price is highly overall correlated with Channels and 3 other fieldsHigh correlation
Order Subtotal is highly overall correlated with Order Tax Total and 4 other fieldsHigh correlation
Order Tax Total is highly overall correlated with Order Subtotal and 4 other fieldsHigh correlation
Order Total is highly overall correlated with Order Subtotal and 4 other fieldsHigh correlation
Recipient Address is highly overall correlated with Channels and 9 other fieldsHigh correlation
Recipient Address 2 is highly overall correlated with Channels and 12 other fieldsHigh correlation
Recipient City is highly overall correlated with Channels and 6 other fieldsHigh correlation
Recipient Country is highly overall correlated with Channels and 8 other fieldsHigh correlation
Recipient Postal Code is highly overall correlated with Channels and 5 other fieldsHigh correlation
Fulfillment Type is highly imbalanced (65.1%) Imbalance
Recipient Address is highly imbalanced (75.5%) Imbalance
Recipient Country is highly imbalanced (51.3%) Imbalance
Order Shipping Price has 1401 (100.0%) missing values Missing
Order Refunded Amount has 1401 (100.0%) missing values Missing
Fulfillment Notes has 1257 (89.7%) missing values Missing
Recipient Email has 334 (23.8%) missing values Missing
Recipient Address has 1046 (74.7%) missing values Missing
Recipient Address 2 has 1362 (97.2%) missing values Missing
Recipient Postal Code has 1046 (74.7%) missing values Missing
Recipient City has 1046 (74.7%) missing values Missing
Recipient Region has 1046 (74.7%) missing values Missing
Recipient Country has 1004 (71.7%) missing values Missing
Item SKU has 1401 (100.0%) missing values Missing
Item Modifiers has 567 (40.5%) missing values Missing
Item Quantity is highly skewed (γ1 = 34.03034393) Skewed
Order Shipping Price is an unsupported type, check if it needs cleaning or further analysis Unsupported
Order Refunded Amount is an unsupported type, check if it needs cleaning or further analysis Unsupported
Item SKU is an unsupported type, check if it needs cleaning or further analysis Unsupported
Item Price has 15 (1.1%) zeros Zeros
Item Options Total Price has 15 (1.1%) zeros Zeros
Item Total Price has 15 (1.1%) zeros Zeros

Reproduction

Analysis started2024-12-19 01:12:11.347803
Analysis finished2024-12-19 01:12:17.293118
Duration5.95 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Order
Text

Distinct256
Distinct (%)18.3%
Missing1
Missing (%)0.1%
Memory size11.1 KiB
2024-12-18T17:12:17.468642image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length23
Mean length15.857857
Min length8

Characters and Unicode

Total characters22201
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)4.2%

Sample

1st rowUber Eats Delivery 8F819
2nd rowUber Eats Delivery 8F819
3rd rowUber Eats Delivery 8F819
4th rowUber Eats Delivery 8F819
5th rowUber Eats Delivery 7751A
ValueCountFrequency (%)
doordash 649
21.6%
square 355
11.8%
online 355
11.8%
delivery 310
10.3%
eats 187
 
6.2%
uber 187
 
6.2%
postmates 147
 
4.9%
link 41
 
1.4%
payment 41
 
1.4%
pickup 24
 
0.8%
Other values (254) 710
23.6%
2024-12-18T17:12:17.733013image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 1726
 
7.8%
e 1705
 
7.7%
O 1695
 
7.6%
1606
 
7.2%
S 1025
 
4.6%
r 852
 
3.8%
n 792
 
3.6%
A 750
 
3.4%
a 730
 
3.3%
i 730
 
3.3%
Other values (34) 10590
47.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22201
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 1726
 
7.8%
e 1705
 
7.7%
O 1695
 
7.6%
1606
 
7.2%
S 1025
 
4.6%
r 852
 
3.8%
n 792
 
3.6%
A 750
 
3.4%
a 730
 
3.3%
i 730
 
3.3%
Other values (34) 10590
47.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22201
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 1726
 
7.8%
e 1705
 
7.7%
O 1695
 
7.6%
1606
 
7.2%
S 1025
 
4.6%
r 852
 
3.8%
n 792
 
3.6%
A 750
 
3.4%
a 730
 
3.3%
i 730
 
3.3%
Other values (34) 10590
47.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22201
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 1726
 
7.8%
e 1705
 
7.7%
O 1695
 
7.6%
1606
 
7.2%
S 1025
 
4.6%
r 852
 
3.8%
n 792
 
3.6%
A 750
 
3.4%
a 730
 
3.3%
i 730
 
3.3%
Other values (34) 10590
47.7%

Order Date
Date

Distinct212
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
Minimum2023-12-28 00:00:00
Maximum2024-12-18 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-18T17:12:17.832874image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:17.937446image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Currency
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
USD
1401 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4203
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 1401
100.0%

Length

2024-12-18T17:12:18.033986image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T17:12:18.099328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
usd 1401
100.0%

Most occurring characters

ValueCountFrequency (%)
U 1401
33.3%
S 1401
33.3%
D 1401
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4203
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 1401
33.3%
S 1401
33.3%
D 1401
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4203
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 1401
33.3%
S 1401
33.3%
D 1401
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4203
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 1401
33.3%
S 1401
33.3%
D 1401
33.3%

Order Subtotal
Real number (ℝ)

High correlation 

Distinct402
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.74571
Minimum5.64
Maximum1039.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2024-12-18T17:12:18.174803image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5.64
5-th percentile19.69
Q135.92
median51.55
Q374.6
95-th percentile174.26
Maximum1039.95
Range1034.31
Interquartile range (IQR)38.68

Descriptive statistics

Standard deviation80.667657
Coefficient of variation (CV)1.1565967
Kurtosis64.062111
Mean69.74571
Median Absolute Deviation (MAD)17.09
Skewness6.6212974
Sum97713.74
Variance6507.2709
MonotonicityNot monotonic
2024-12-18T17:12:18.273323image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103.15 15
 
1.1%
19.99 12
 
0.9%
205.01 12
 
0.9%
175.44 11
 
0.8%
461.07 10
 
0.7%
58.04 10
 
0.7%
57.86 10
 
0.7%
44.27 10
 
0.7%
93.28 10
 
0.7%
98.22 10
 
0.7%
Other values (392) 1291
92.1%
ValueCountFrequency (%)
5.64 2
0.1%
8.49 1
 
0.1%
8.79 1
 
0.1%
9.24 1
 
0.1%
9.34 1
 
0.1%
9.48 1
 
0.1%
11.19 1
 
0.1%
11.22 1
 
0.1%
11.92 2
0.1%
13.32 4
0.3%
ValueCountFrequency (%)
1039.95 4
 
0.3%
461.07 10
0.7%
450 1
 
0.1%
444.85 2
 
0.1%
358.35 9
0.6%
357.98 2
 
0.1%
318.67 8
0.6%
259.61 1
 
0.1%
211.26 3
 
0.2%
205.01 12
0.9%

Order Shipping Price
Unsupported

Missing  Rejected  Unsupported 

Missing1401
Missing (%)100.0%
Memory size11.1 KiB

Order Tax Total
Real number (ℝ)

High correlation 

Distinct332
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3773091
Minimum0.54
Maximum98.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2024-12-18T17:12:18.374237image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.54
5-th percentile1.85
Q13.03
median4.6
Q36.79
95-th percentile16.08
Maximum98.8
Range98.26
Interquartile range (IQR)3.76

Descriptive statistics

Standard deviation7.6664224
Coefficient of variation (CV)1.2021406
Kurtosis64.795293
Mean6.3773091
Median Absolute Deviation (MAD)1.72
Skewness6.6814653
Sum8934.61
Variance58.774033
MonotonicityNot monotonic
2024-12-18T17:12:18.470280image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.46 19
 
1.4%
9.32 15
 
1.1%
3.32 15
 
1.1%
5.23 13
 
0.9%
17.86 12
 
0.9%
2.94 12
 
0.9%
8.09 12
 
0.9%
16.38 11
 
0.8%
5.04 10
 
0.7%
4.21 10
 
0.7%
Other values (322) 1272
90.8%
ValueCountFrequency (%)
0.54 2
0.1%
0.81 1
0.1%
0.84 1
0.1%
0.88 1
0.1%
0.89 1
0.1%
0.9 1
0.1%
1.06 1
0.1%
1.07 1
0.1%
1.13 2
0.1%
1.19 2
0.1%
ValueCountFrequency (%)
98.8 4
 
0.3%
43.8 10
0.7%
42.75 1
 
0.1%
42.26 2
 
0.1%
34.04 9
0.6%
34.01 2
 
0.1%
30.27 8
0.6%
24.19 1
 
0.1%
20.07 3
 
0.2%
17.86 12
0.9%

Order Total
Real number (ℝ)

High correlation 

Distinct432
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.999629
Minimum6.18
Maximum1158.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2024-12-18T17:12:18.561468image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum6.18
5-th percentile21.89
Q138.92
median58.19
Q383.28
95-th percentile191.82
Maximum1158.75
Range1152.57
Interquartile range (IQR)44.36

Descriptive statistics

Standard deviation90.501046
Coefficient of variation (CV)1.1602753
Kurtosis62.219519
Mean77.999629
Median Absolute Deviation (MAD)20.14
Skewness6.5143436
Sum109277.48
Variance8190.4392
MonotonicityNot monotonic
2024-12-18T17:12:18.657372image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112.47 15
 
1.1%
243.37 12
 
0.9%
191.82 11
 
0.8%
63.08 10
 
0.7%
48.48 10
 
0.7%
107.08 10
 
0.7%
21.89 10
 
0.7%
101.1 10
 
0.7%
504.87 10
 
0.7%
42.95 9
 
0.6%
Other values (422) 1294
92.4%
ValueCountFrequency (%)
6.18 2
0.1%
9.3 1
 
0.1%
9.63 1
 
0.1%
10.12 1
 
0.1%
10.23 1
 
0.1%
10.38 1
 
0.1%
12.25 1
 
0.1%
13.41 1
 
0.1%
14.59 4
0.3%
15.71 1
 
0.1%
ValueCountFrequency (%)
1158.75 4
 
0.3%
512.75 1
 
0.1%
504.87 10
0.7%
487.11 2
 
0.1%
427.79 2
 
0.1%
412.01 9
0.6%
363.94 8
0.6%
283.8 1
 
0.1%
252.46 3
 
0.2%
243.37 12
0.9%

Order Refunded Amount
Unsupported

Missing  Rejected  Unsupported 

Missing1401
Missing (%)100.0%
Memory size11.1 KiB

Fulfillment Date
Date

Distinct509
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
Minimum2023-12-28 13:30:00
Maximum2024-12-18 13:58:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-18T17:12:18.746265image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:18.842197image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Fulfillment Type
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
Pickup
1237 
Curbside
 
79
Delivery
 
43
Other
 
42

Length

Max length8
Median length6
Mean length6.1441827
Min length5

Characters and Unicode

Total characters8608
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPickup
2nd rowPickup
3rd rowPickup
4th rowPickup
5th rowPickup

Common Values

ValueCountFrequency (%)
Pickup 1237
88.3%
Curbside 79
 
5.6%
Delivery 43
 
3.1%
Other 42
 
3.0%

Length

2024-12-18T17:12:18.939013image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T17:12:19.015709image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
pickup 1237
88.3%
curbside 79
 
5.6%
delivery 43
 
3.1%
other 42
 
3.0%

Most occurring characters

ValueCountFrequency (%)
i 1359
15.8%
u 1316
15.3%
P 1237
14.4%
c 1237
14.4%
k 1237
14.4%
p 1237
14.4%
e 207
 
2.4%
r 164
 
1.9%
C 79
 
0.9%
s 79
 
0.9%
Other values (9) 456
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1359
15.8%
u 1316
15.3%
P 1237
14.4%
c 1237
14.4%
k 1237
14.4%
p 1237
14.4%
e 207
 
2.4%
r 164
 
1.9%
C 79
 
0.9%
s 79
 
0.9%
Other values (9) 456
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1359
15.8%
u 1316
15.3%
P 1237
14.4%
c 1237
14.4%
k 1237
14.4%
p 1237
14.4%
e 207
 
2.4%
r 164
 
1.9%
C 79
 
0.9%
s 79
 
0.9%
Other values (9) 456
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1359
15.8%
u 1316
15.3%
P 1237
14.4%
c 1237
14.4%
k 1237
14.4%
p 1237
14.4%
e 207
 
2.4%
r 164
 
1.9%
C 79
 
0.9%
s 79
 
0.9%
Other values (9) 456
 
5.3%

Fulfillment Status
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
Completed
1401 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters12609
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompleted
2nd rowCompleted
3rd rowCompleted
4th rowCompleted
5th rowCompleted

Common Values

ValueCountFrequency (%)
Completed 1401
100.0%

Length

2024-12-18T17:12:19.093306image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T17:12:19.159282image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
completed 1401
100.0%

Most occurring characters

ValueCountFrequency (%)
e 2802
22.2%
C 1401
11.1%
m 1401
11.1%
o 1401
11.1%
p 1401
11.1%
l 1401
11.1%
t 1401
11.1%
d 1401
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12609
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2802
22.2%
C 1401
11.1%
m 1401
11.1%
o 1401
11.1%
p 1401
11.1%
l 1401
11.1%
t 1401
11.1%
d 1401
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12609
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2802
22.2%
C 1401
11.1%
m 1401
11.1%
o 1401
11.1%
p 1401
11.1%
l 1401
11.1%
t 1401
11.1%
d 1401
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12609
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2802
22.2%
C 1401
11.1%
m 1401
11.1%
o 1401
11.1%
p 1401
11.1%
l 1401
11.1%
t 1401
11.1%
d 1401
11.1%

Channels
Categorical

High correlation 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
DoorDash
670 
BELLY RUBB | BBQ Catering | Barbecue To Go and Delivery
355 
Postmates Delivery
334 
Payment Links
 
41
Belly Rubb
 
1

Length

Max length55
Median length18
Mean length22.441113
Min length8

Characters and Unicode

Total characters31440
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowPostmates Delivery
2nd rowPostmates Delivery
3rd rowPostmates Delivery
4th rowPostmates Delivery
5th rowPostmates Delivery

Common Values

ValueCountFrequency (%)
DoorDash 670
47.8%
BELLY RUBB | BBQ Catering | Barbecue To Go and Delivery 355
25.3%
Postmates Delivery 334
23.8%
Payment Links 41
 
2.9%
Belly Rubb 1
 
0.1%

Length

2024-12-18T17:12:19.229118image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T17:12:19.306415image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
710
13.3%
delivery 689
12.9%
doordash 670
12.6%
belly 356
6.7%
rubb 356
6.7%
bbq 355
6.7%
catering 355
6.7%
barbecue 355
6.7%
go 355
6.7%
to 355
6.7%
Other values (4) 771
14.5%

Most occurring characters

ValueCountFrequency (%)
3926
 
12.5%
e 2819
 
9.0%
o 2384
 
7.6%
B 2131
 
6.8%
a 2110
 
6.7%
r 2069
 
6.6%
D 2029
 
6.5%
s 1379
 
4.4%
i 1085
 
3.5%
t 1064
 
3.4%
Other values (23) 10444
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31440
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3926
 
12.5%
e 2819
 
9.0%
o 2384
 
7.6%
B 2131
 
6.8%
a 2110
 
6.7%
r 2069
 
6.6%
D 2029
 
6.5%
s 1379
 
4.4%
i 1085
 
3.5%
t 1064
 
3.4%
Other values (23) 10444
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31440
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3926
 
12.5%
e 2819
 
9.0%
o 2384
 
7.6%
B 2131
 
6.8%
a 2110
 
6.7%
r 2069
 
6.6%
D 2029
 
6.5%
s 1379
 
4.4%
i 1085
 
3.5%
t 1064
 
3.4%
Other values (23) 10444
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31440
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3926
 
12.5%
e 2819
 
9.0%
o 2384
 
7.6%
B 2131
 
6.8%
a 2110
 
6.7%
r 2069
 
6.6%
D 2029
 
6.5%
s 1379
 
4.4%
i 1085
 
3.5%
t 1064
 
3.4%
Other values (23) 10444
33.2%

Fulfillment Location
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
Belly Rubb
1401 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters14010
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBelly Rubb
2nd rowBelly Rubb
3rd rowBelly Rubb
4th rowBelly Rubb
5th rowBelly Rubb

Common Values

ValueCountFrequency (%)
Belly Rubb 1401
100.0%

Length

2024-12-18T17:12:19.393434image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T17:12:19.459373image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
belly 1401
50.0%
rubb 1401
50.0%

Most occurring characters

ValueCountFrequency (%)
l 2802
20.0%
b 2802
20.0%
e 1401
10.0%
B 1401
10.0%
y 1401
10.0%
1401
10.0%
R 1401
10.0%
u 1401
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14010
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2802
20.0%
b 2802
20.0%
e 1401
10.0%
B 1401
10.0%
y 1401
10.0%
1401
10.0%
R 1401
10.0%
u 1401
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14010
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2802
20.0%
b 2802
20.0%
e 1401
10.0%
B 1401
10.0%
y 1401
10.0%
1401
10.0%
R 1401
10.0%
u 1401
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14010
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2802
20.0%
b 2802
20.0%
e 1401
10.0%
B 1401
10.0%
y 1401
10.0%
1401
10.0%
R 1401
10.0%
u 1401
10.0%

Fulfillment Notes
Text

Missing 

Distinct52
Distinct (%)36.1%
Missing1257
Missing (%)89.7%
Memory size11.1 KiB
2024-12-18T17:12:19.623901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length396
Median length182
Mean length108.0625
Min length12

Characters and Unicode

Total characters15561
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)9.7%

Sample

1st rowCURBSIDE PICKUP - DETAILS: White Chrysler minivan
2nd rowNo Contact Order. Delivery Instructions: PLEASE CALL 818 912 3845 Juan Jose Munoz will answer Delivery By Doordash. Courier pickup time: 11/28/2024 3:35pm - 3:45pm. For issues, contact 1(855) 222-8111 and provide the following delivery ID: 2376523971. Total Fees Applied: $17,29
3rd rowForks please
4th rowForks please
5th rowForks please
ValueCountFrequency (%)
139
 
5.7%
pickup 124
 
5.1%
delivery 104
 
4.2%
the 93
 
3.8%
curbside 79
 
3.2%
and 72
 
2.9%
details 70
 
2.9%
for 52
 
2.1%
issues 48
 
2.0%
contact 47
 
1.9%
Other values (247) 1624
66.2%
2024-12-18T17:12:19.933929image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2236
 
14.4%
e 1019
 
6.5%
o 633
 
4.1%
i 623
 
4.0%
a 606
 
3.9%
r 555
 
3.6%
t 515
 
3.3%
s 485
 
3.1%
l 485
 
3.1%
n 405
 
2.6%
Other values (65) 7999
51.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15561
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2236
 
14.4%
e 1019
 
6.5%
o 633
 
4.1%
i 623
 
4.0%
a 606
 
3.9%
r 555
 
3.6%
t 515
 
3.3%
s 485
 
3.1%
l 485
 
3.1%
n 405
 
2.6%
Other values (65) 7999
51.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15561
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2236
 
14.4%
e 1019
 
6.5%
o 633
 
4.1%
i 623
 
4.0%
a 606
 
3.9%
r 555
 
3.6%
t 515
 
3.3%
s 485
 
3.1%
l 485
 
3.1%
n 405
 
2.6%
Other values (65) 7999
51.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15561
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2236
 
14.4%
e 1019
 
6.5%
o 633
 
4.1%
i 623
 
4.0%
a 606
 
3.9%
r 555
 
3.6%
t 515
 
3.3%
s 485
 
3.1%
l 485
 
3.1%
n 405
 
2.6%
Other values (65) 7999
51.4%

Recipient Name
Text

Distinct367
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
2024-12-18T17:12:20.072161image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length18
Mean length11.127052
Min length4

Characters and Unicode

Total characters15589
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)5.1%

Sample

1st row8F819-Diana O.
2nd row8F819-Diana O.
3rd row8F819-Diana O.
4th row8F819-Diana O.
5th row7751A-Lilit A.
ValueCountFrequency (%)
s 125
 
4.4%
d 71
 
2.5%
k 71
 
2.5%
b 71
 
2.5%
c 68
 
2.4%
m 66
 
2.3%
a 66
 
2.3%
o 63
 
2.2%
g 54
 
1.9%
l 53
 
1.9%
Other values (434) 2108
74.9%
2024-12-18T17:12:20.519581image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1415
 
9.1%
a 1308
 
8.4%
e 1059
 
6.8%
n 961
 
6.2%
i 728
 
4.7%
r 706
 
4.5%
o 542
 
3.5%
l 486
 
3.1%
- 382
 
2.5%
A 376
 
2.4%
Other values (54) 7626
48.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15589
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1415
 
9.1%
a 1308
 
8.4%
e 1059
 
6.8%
n 961
 
6.2%
i 728
 
4.7%
r 706
 
4.5%
o 542
 
3.5%
l 486
 
3.1%
- 382
 
2.5%
A 376
 
2.4%
Other values (54) 7626
48.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15589
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1415
 
9.1%
a 1308
 
8.4%
e 1059
 
6.8%
n 961
 
6.2%
i 728
 
4.7%
r 706
 
4.5%
o 542
 
3.5%
l 486
 
3.1%
- 382
 
2.5%
A 376
 
2.4%
Other values (54) 7626
48.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15589
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1415
 
9.1%
a 1308
 
8.4%
e 1059
 
6.8%
n 961
 
6.2%
i 728
 
4.7%
r 706
 
4.5%
o 542
 
3.5%
l 486
 
3.1%
- 382
 
2.5%
A 376
 
2.4%
Other values (54) 7626
48.9%

Recipient Email
Text

Missing 

Distinct97
Distinct (%)9.1%
Missing334
Missing (%)23.8%
Memory size11.1 KiB
2024-12-18T17:12:20.611471image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length38
Median length38
Mean length32
Min length13

Characters and Unicode

Total characters34144
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)2.0%

Sample

1st rowpoint-of-sale-integration@doordash.com
2nd rowpoint-of-sale-integration@doordash.com
3rd rowpoint-of-sale-integration@doordash.com
4th rowpoint-of-sale-integration@doordash.com
5th rowpoint-of-sale-integration@doordash.com
ValueCountFrequency (%)
point-of-sale-integration@doordash.com 670
62.8%
alex.anthony.diaz@gmail.com 31
 
2.9%
steven.trella@gmail.com 28
 
2.6%
mogshut@gmail.com 28
 
2.6%
bidium@gmail.com 22
 
2.1%
monalapides@gmail.com 12
 
1.1%
m.m.keshishyan@gmail.com 11
 
1.0%
strwbrytiff@yahoo.com 10
 
0.9%
narumol2003@yahoo.com 8
 
0.7%
thomsteltime@gmail.com 7
 
0.7%
Other values (87) 240
 
22.5%
2024-12-18T17:12:20.783670image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 4769
14.0%
a 2878
 
8.4%
i 2607
 
7.6%
n 2388
 
7.0%
t 2292
 
6.7%
- 2010
 
5.9%
e 1783
 
5.2%
r 1618
 
4.7%
s 1548
 
4.5%
m 1545
 
4.5%
Other values (45) 10706
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34144
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 4769
14.0%
a 2878
 
8.4%
i 2607
 
7.6%
n 2388
 
7.0%
t 2292
 
6.7%
- 2010
 
5.9%
e 1783
 
5.2%
r 1618
 
4.7%
s 1548
 
4.5%
m 1545
 
4.5%
Other values (45) 10706
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34144
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 4769
14.0%
a 2878
 
8.4%
i 2607
 
7.6%
n 2388
 
7.0%
t 2292
 
6.7%
- 2010
 
5.9%
e 1783
 
5.2%
r 1618
 
4.7%
s 1548
 
4.5%
m 1545
 
4.5%
Other values (45) 10706
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34144
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 4769
14.0%
a 2878
 
8.4%
i 2607
 
7.6%
n 2388
 
7.0%
t 2292
 
6.7%
- 2010
 
5.9%
e 1783
 
5.2%
r 1618
 
4.7%
s 1548
 
4.5%
m 1545
 
4.5%
Other values (45) 10706
31.4%

Recipient Phone
Text

Distinct95
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
2024-12-18T17:12:20.904680image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length12
Mean length11.810136
Min length10

Characters and Unicode

Total characters16546
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)1.4%

Sample

1st row+1 312-766-6835
2nd row+1 312-766-6835
3rd row+1 312-766-6835
4th row+1 312-766-6835
5th row+1 312-766-6835
ValueCountFrequency (%)
1 358
20.4%
8552228111 357
20.3%
312-766-6835 334
19.0%
8559731040 313
17.8%
16268647315 35
 
2.0%
16266164211 31
 
1.8%
18184862439 28
 
1.6%
818-822-5060 22
 
1.3%
18186360644 12
 
0.7%
17472562597 11
 
0.6%
Other values (86) 258
14.7%
2024-12-18T17:12:21.123988image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2978
18.0%
5 1985
12.0%
8 1979
12.0%
2 1864
11.3%
6 1632
9.9%
3 1308
7.9%
7 930
 
5.6%
0 866
 
5.2%
+ 731
 
4.4%
- 716
 
4.3%
Other values (3) 1557
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16546
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2978
18.0%
5 1985
12.0%
8 1979
12.0%
2 1864
11.3%
6 1632
9.9%
3 1308
7.9%
7 930
 
5.6%
0 866
 
5.2%
+ 731
 
4.4%
- 716
 
4.3%
Other values (3) 1557
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16546
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2978
18.0%
5 1985
12.0%
8 1979
12.0%
2 1864
11.3%
6 1632
9.9%
3 1308
7.9%
7 930
 
5.6%
0 866
 
5.2%
+ 731
 
4.4%
- 716
 
4.3%
Other values (3) 1557
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16546
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2978
18.0%
5 1985
12.0%
8 1979
12.0%
2 1864
11.3%
6 1632
9.9%
3 1308
7.9%
7 930
 
5.6%
0 866
 
5.2%
+ 731
 
4.4%
- 716
 
4.3%
Other values (3) 1557
9.4%

Recipient Address
Categorical

High correlation  Imbalance  Missing 

Distinct15
Distinct (%)4.2%
Missing1046
Missing (%)74.7%
Memory size11.1 KiB
13346 Saticoy St
312 
6615 Sepulveda Boulevard
 
9
15447 Tupper Street
 
5
15825 Saticoy Street
 
5
5447 Matilija Avenue
 
4
Other values (10)
 
20

Length

Max length24
Median length16
Mean length16.63662
Min length16

Characters and Unicode

Total characters5906
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)1.1%

Sample

1st row13346 Saticoy St
2nd row13346 Saticoy St
3rd row13346 Saticoy St
4th row13346 Saticoy St
5th row13346 Saticoy St

Common Values

ValueCountFrequency (%)
13346 Saticoy St 312
 
22.3%
6615 Sepulveda Boulevard 9
 
0.6%
15447 Tupper Street 5
 
0.4%
15825 Saticoy Street 5
 
0.4%
5447 Matilija Avenue 4
 
0.3%
12320 Burbank Boulevard 3
 
0.2%
6230 Van Nuys Boulevard 3
 
0.2%
13244 Arminta Street 3
 
0.2%
7636 Fulton Avenue 3
 
0.2%
9017 Greenbush Avenue 2
 
0.1%
Other values (5) 6
 
0.4%
(Missing) 1046
74.7%

Length

2024-12-18T17:12:21.227197image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
saticoy 317
29.6%
13346 312
29.2%
st 312
29.2%
boulevard 18
 
1.7%
street 14
 
1.3%
avenue 11
 
1.0%
sepulveda 9
 
0.8%
6615 9
 
0.8%
15447 5
 
0.5%
tupper 5
 
0.5%
Other values (23) 58
 
5.4%

Most occurring characters

ValueCountFrequency (%)
715
12.1%
t 670
11.3%
S 652
11.0%
3 639
10.8%
a 365
 
6.2%
1 342
 
5.8%
o 342
 
5.8%
6 341
 
5.8%
4 337
 
5.7%
i 330
 
5.6%
Other values (35) 1173
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5906
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
715
12.1%
t 670
11.3%
S 652
11.0%
3 639
10.8%
a 365
 
6.2%
1 342
 
5.8%
o 342
 
5.8%
6 341
 
5.8%
4 337
 
5.7%
i 330
 
5.6%
Other values (35) 1173
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5906
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
715
12.1%
t 670
11.3%
S 652
11.0%
3 639
10.8%
a 365
 
6.2%
1 342
 
5.8%
o 342
 
5.8%
6 341
 
5.8%
4 337
 
5.7%
i 330
 
5.6%
Other values (35) 1173
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5906
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
715
12.1%
t 670
11.3%
S 652
11.0%
3 639
10.8%
a 365
 
6.2%
1 342
 
5.8%
o 342
 
5.8%
6 341
 
5.8%
4 337
 
5.7%
i 330
 
5.6%
Other values (35) 1173
19.9%

Recipient Address 2
Categorical

High correlation  Missing 

Distinct7
Distinct (%)17.9%
Missing1362
Missing (%)97.2%
Memory size11.1 KiB
unit 1
17 
104
9 
25
5 
123
3 
Apt 314
3 
Other values (2)
2 

Length

Max length14
Median length7
Mean length4.7692308
Min length2

Characters and Unicode

Total characters186
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)5.1%

Sample

1st row214
2nd row104
3rd row104
4th row104
5th row104

Common Values

ValueCountFrequency (%)
unit 1 17
 
1.2%
104 9
 
0.6%
25 5
 
0.4%
123 3
 
0.2%
Apt 314 3
 
0.2%
214 1
 
0.1%
Leasing office 1
 
0.1%
(Missing) 1362
97.2%

Length

2024-12-18T17:12:21.321657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T17:12:21.404114image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
unit 17
28.3%
1 17
28.3%
104 9
15.0%
25 5
 
8.3%
123 3
 
5.0%
apt 3
 
5.0%
314 3
 
5.0%
214 1
 
1.7%
leasing 1
 
1.7%
office 1
 
1.7%

Most occurring characters

ValueCountFrequency (%)
1 33
17.7%
21
11.3%
t 20
10.8%
i 19
10.2%
n 18
9.7%
u 17
9.1%
4 13
 
7.0%
0 9
 
4.8%
2 9
 
4.8%
3 6
 
3.2%
Other values (11) 21
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 33
17.7%
21
11.3%
t 20
10.8%
i 19
10.2%
n 18
9.7%
u 17
9.1%
4 13
 
7.0%
0 9
 
4.8%
2 9
 
4.8%
3 6
 
3.2%
Other values (11) 21
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 33
17.7%
21
11.3%
t 20
10.8%
i 19
10.2%
n 18
9.7%
u 17
9.1%
4 13
 
7.0%
0 9
 
4.8%
2 9
 
4.8%
3 6
 
3.2%
Other values (11) 21
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 186
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 33
17.7%
21
11.3%
t 20
10.8%
i 19
10.2%
n 18
9.7%
u 17
9.1%
4 13
 
7.0%
0 9
 
4.8%
2 9
 
4.8%
3 6
 
3.2%
Other values (11) 21
11.3%

Recipient Postal Code
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)3.1%
Missing1046
Missing (%)74.7%
Infinite0
Infinite (%)0.0%
Mean91586.335
Minimum91331
Maximum91607
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2024-12-18T17:12:21.480470image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum91331
5-th percentile91406
Q191605
median91605
Q391605
95-th percentile91605
Maximum91607
Range276
Interquartile range (IQR)0

Descriptive statistics

Standard deviation61.040394
Coefficient of variation (CV)0.00066647927
Kurtosis7.6206666
Mean91586.335
Median Absolute Deviation (MAD)0
Skewness-3.0454406
Sum32513149
Variance3725.9297
MonotonicityNot monotonic
2024-12-18T17:12:21.554392image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
91605 318
 
22.7%
91411 9
 
0.6%
91401 7
 
0.5%
91406 5
 
0.4%
91343 5
 
0.4%
91607 3
 
0.2%
91606 2
 
0.1%
91405 2
 
0.1%
91331 2
 
0.1%
91601 1
 
0.1%
(Missing) 1046
74.7%
ValueCountFrequency (%)
91331 2
 
0.1%
91343 5
 
0.4%
91401 7
 
0.5%
91402 1
 
0.1%
91405 2
 
0.1%
91406 5
 
0.4%
91411 9
 
0.6%
91601 1
 
0.1%
91605 318
22.7%
91606 2
 
0.1%
ValueCountFrequency (%)
91607 3
 
0.2%
91606 2
 
0.1%
91605 318
22.7%
91601 1
 
0.1%
91411 9
 
0.6%
91406 5
 
0.4%
91405 2
 
0.1%
91402 1
 
0.1%
91401 7
 
0.5%
91343 5
 
0.4%

Recipient City
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.6%
Missing1046
Missing (%)74.7%
Memory size11.1 KiB
North Hollywood
295 
Los Angeles
60 

Length

Max length15
Median length15
Mean length14.323944
Min length11

Characters and Unicode

Total characters5085
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth Hollywood
2nd rowNorth Hollywood
3rd rowNorth Hollywood
4th rowNorth Hollywood
5th rowNorth Hollywood

Common Values

ValueCountFrequency (%)
North Hollywood 295
 
21.1%
Los Angeles 60
 
4.3%
(Missing) 1046
74.7%

Length

2024-12-18T17:12:21.652120image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T17:12:21.733434image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
north 295
41.5%
hollywood 295
41.5%
los 60
 
8.5%
angeles 60
 
8.5%

Most occurring characters

ValueCountFrequency (%)
o 1240
24.4%
l 650
12.8%
355
 
7.0%
N 295
 
5.8%
r 295
 
5.8%
h 295
 
5.8%
t 295
 
5.8%
H 295
 
5.8%
y 295
 
5.8%
w 295
 
5.8%
Other values (7) 775
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5085
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1240
24.4%
l 650
12.8%
355
 
7.0%
N 295
 
5.8%
r 295
 
5.8%
h 295
 
5.8%
t 295
 
5.8%
H 295
 
5.8%
y 295
 
5.8%
w 295
 
5.8%
Other values (7) 775
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5085
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1240
24.4%
l 650
12.8%
355
 
7.0%
N 295
 
5.8%
r 295
 
5.8%
h 295
 
5.8%
t 295
 
5.8%
H 295
 
5.8%
y 295
 
5.8%
w 295
 
5.8%
Other values (7) 775
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5085
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1240
24.4%
l 650
12.8%
355
 
7.0%
N 295
 
5.8%
r 295
 
5.8%
h 295
 
5.8%
t 295
 
5.8%
H 295
 
5.8%
y 295
 
5.8%
w 295
 
5.8%
Other values (7) 775
15.2%

Recipient Region
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.3%
Missing1046
Missing (%)74.7%
Memory size11.1 KiB
CA
355 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters710
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCA
2nd rowCA
3rd rowCA
4th rowCA
5th rowCA

Common Values

ValueCountFrequency (%)
CA 355
 
25.3%
(Missing) 1046
74.7%

Length

2024-12-18T17:12:21.811978image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T17:12:21.882377image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ca 355
100.0%

Most occurring characters

ValueCountFrequency (%)
C 355
50.0%
A 355
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 710
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 355
50.0%
A 355
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 710
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 355
50.0%
A 355
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 710
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 355
50.0%
A 355
50.0%

Recipient Country
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.5%
Missing1004
Missing (%)71.7%
Memory size11.1 KiB
US
355 
ZZ
42 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters794
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
US 355
 
25.3%
ZZ 42
 
3.0%
(Missing) 1004
71.7%

Length

2024-12-18T17:12:21.952011image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T17:12:22.055925image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
us 355
89.4%
zz 42
 
10.6%

Most occurring characters

ValueCountFrequency (%)
U 355
44.7%
S 355
44.7%
Z 84
 
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 794
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 355
44.7%
S 355
44.7%
Z 84
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 794
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 355
44.7%
S 355
44.7%
Z 84
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 794
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 355
44.7%
S 355
44.7%
Z 84
 
10.6%

Item Quantity
Real number (ℝ)

High correlation  Skewed 

Distinct13
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.276945
Minimum1
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2024-12-18T17:12:22.175921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum130
Range129
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.5579434
Coefficient of variation (CV)2.7862933
Kurtosis1226.54
Mean1.276945
Median Absolute Deviation (MAD)0
Skewness34.030344
Sum1789
Variance12.658961
MonotonicityNot monotonic
2024-12-18T17:12:22.262020image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 1265
90.3%
2 96
 
6.9%
3 13
 
0.9%
4 13
 
0.9%
5 4
 
0.3%
15 2
 
0.1%
6 2
 
0.1%
8 1
 
0.1%
11 1
 
0.1%
14 1
 
0.1%
Other values (3) 3
 
0.2%
ValueCountFrequency (%)
1 1265
90.3%
2 96
 
6.9%
3 13
 
0.9%
4 13
 
0.9%
5 4
 
0.3%
6 2
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
11 1
 
0.1%
ValueCountFrequency (%)
130 1
 
0.1%
15 2
 
0.1%
14 1
 
0.1%
11 1
 
0.1%
9 1
 
0.1%
8 1
 
0.1%
7 1
 
0.1%
6 2
 
0.1%
5 4
 
0.3%
4 13
0.9%

Item Name
Text

Distinct75
Distinct (%)5.4%
Missing1
Missing (%)0.1%
Memory size11.1 KiB
2024-12-18T17:12:22.402372image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length31
Median length25
Mean length17.597857
Min length7

Characters and Unicode

Total characters24637
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.9%

Sample

1st rowCRISPY CHICKEN SANDWICH
2nd rowLOADED FRIES
3rd rowCREAMY BLUE CHEESE DIP
4th rowARTISAN MAC AND CHEESE
5th rowBEEF SHORT RIB
ValueCountFrequency (%)
back 284
 
6.6%
baby 280
 
6.6%
ribs 218
 
5.1%
belly 166
 
3.9%
pork 165
 
3.9%
glazed 165
 
3.9%
fries 148
 
3.5%
combo 143
 
3.3%
rib 139
 
3.3%
beef 118
 
2.8%
Other values (100) 2446
57.3%
2024-12-18T17:12:22.632279image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2872
 
11.7%
E 2260
 
9.2%
B 1922
 
7.8%
A 1845
 
7.5%
S 1487
 
6.0%
R 1413
 
5.7%
I 1405
 
5.7%
C 1391
 
5.6%
L 1114
 
4.5%
O 981
 
4.0%
Other values (55) 7947
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2872
 
11.7%
E 2260
 
9.2%
B 1922
 
7.8%
A 1845
 
7.5%
S 1487
 
6.0%
R 1413
 
5.7%
I 1405
 
5.7%
C 1391
 
5.6%
L 1114
 
4.5%
O 981
 
4.0%
Other values (55) 7947
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2872
 
11.7%
E 2260
 
9.2%
B 1922
 
7.8%
A 1845
 
7.5%
S 1487
 
6.0%
R 1413
 
5.7%
I 1405
 
5.7%
C 1391
 
5.6%
L 1114
 
4.5%
O 981
 
4.0%
Other values (55) 7947
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2872
 
11.7%
E 2260
 
9.2%
B 1922
 
7.8%
A 1845
 
7.5%
S 1487
 
6.0%
R 1413
 
5.7%
I 1405
 
5.7%
C 1391
 
5.6%
L 1114
 
4.5%
O 981
 
4.0%
Other values (55) 7947
32.3%

Item SKU
Unsupported

Missing  Rejected  Unsupported 

Missing1401
Missing (%)100.0%
Memory size11.1 KiB

Item Variation
Categorical

Distinct25
Distinct (%)1.8%
Missing1
Missing (%)0.1%
Memory size11.1 KiB
Regular
721 
Full Rack
129 
Side
115 
Full
100 
4 Bites
80 
Other values (20)
255 

Length

Max length39
Median length7
Mean length6.9221429
Min length4

Characters and Unicode

Total characters9691
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowRegular
2nd rowRegular
3rd rowRegular
4th rowSide
5th rowRegular

Common Values

ValueCountFrequency (%)
Regular 721
51.5%
Full Rack 129
 
9.2%
Side 115
 
8.2%
Full 100
 
7.1%
4 Bites 80
 
5.7%
Half rack 52
 
3.7%
6 pcs 51
 
3.6%
8 pcs 26
 
1.9%
12 pcs 25
 
1.8%
2 sliders 23
 
1.6%
Other values (15) 78
 
5.6%

Length

2024-12-18T17:12:22.738633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
regular 721
38.2%
full 236
 
12.5%
rack 181
 
9.6%
side 129
 
6.8%
pcs 120
 
6.4%
bites 101
 
5.3%
4 90
 
4.8%
6 58
 
3.1%
half 54
 
2.9%
sliders 35
 
1.9%
Other values (13) 163
 
8.6%

Most occurring characters

ValueCountFrequency (%)
l 1307
13.5%
e 1042
10.8%
a 984
10.2%
u 969
10.0%
R 863
8.9%
r 848
8.8%
g 721
 
7.4%
488
 
5.0%
s 316
 
3.3%
c 302
 
3.1%
Other values (29) 1851
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 1307
13.5%
e 1042
10.8%
a 984
10.2%
u 969
10.0%
R 863
8.9%
r 848
8.8%
g 721
 
7.4%
488
 
5.0%
s 316
 
3.3%
c 302
 
3.1%
Other values (29) 1851
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 1307
13.5%
e 1042
10.8%
a 984
10.2%
u 969
10.0%
R 863
8.9%
r 848
8.8%
g 721
 
7.4%
488
 
5.0%
s 316
 
3.3%
c 302
 
3.1%
Other values (29) 1851
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 1307
13.5%
e 1042
10.8%
a 984
10.2%
u 969
10.0%
R 863
8.9%
r 848
8.8%
g 721
 
7.4%
488
 
5.0%
s 316
 
3.3%
c 302
 
3.1%
Other values (29) 1851
19.1%

Item Modifiers
Text

Missing 

Distinct393
Distinct (%)47.1%
Missing567
Missing (%)40.5%
Memory size11.1 KiB
2024-12-18T17:12:22.882085image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length236
Median length145.5
Mean length56.443645
Min length11

Characters and Unicode

Total characters47074
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique300 ?
Unique (%)36.0%

Sample

1st row1 x Signature BBQ Sauce Drizzle, 1 x Salt and Pepper
2nd row1 x Lemon Pepper
3rd row1 x Sprite ™, 1 x Mac & Cheese, 1 x Rosemary Pepper Fries, 1 x Sweet&Spicy Glaze, 1 x Boom-Boom Sauce DIp
4th row1 x Boom-Boom Sauce DIp, 1 x Sweet and Spicy BBQ Sauce Dip
5th row1 x Signature BBQ Sauce Drizzle, 1 x Salt and Pepper
ValueCountFrequency (%)
1 1928
19.2%
x 1928
19.2%
bbq 518
 
5.2%
glaze 453
 
4.5%
signature 419
 
4.2%
sauce 291
 
2.9%
dip 286
 
2.9%
pepper 259
 
2.6%
no 176
 
1.8%
salt 141
 
1.4%
Other values (112) 3631
36.2%
2024-12-18T17:12:23.157972image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9196
19.5%
e 4557
 
9.7%
a 2878
 
6.1%
x 1977
 
4.2%
1 1928
 
4.1%
p 1668
 
3.5%
l 1565
 
3.3%
i 1555
 
3.3%
r 1551
 
3.3%
S 1404
 
3.0%
Other values (50) 18795
39.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47074
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9196
19.5%
e 4557
 
9.7%
a 2878
 
6.1%
x 1977
 
4.2%
1 1928
 
4.1%
p 1668
 
3.5%
l 1565
 
3.3%
i 1555
 
3.3%
r 1551
 
3.3%
S 1404
 
3.0%
Other values (50) 18795
39.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47074
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9196
19.5%
e 4557
 
9.7%
a 2878
 
6.1%
x 1977
 
4.2%
1 1928
 
4.1%
p 1668
 
3.5%
l 1565
 
3.3%
i 1555
 
3.3%
r 1551
 
3.3%
S 1404
 
3.0%
Other values (50) 18795
39.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47074
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9196
19.5%
e 4557
 
9.7%
a 2878
 
6.1%
x 1977
 
4.2%
1 1928
 
4.1%
p 1668
 
3.5%
l 1565
 
3.3%
i 1555
 
3.3%
r 1551
 
3.3%
S 1404
 
3.0%
Other values (50) 18795
39.9%

Item Price
Real number (ℝ)

High correlation  Zeros 

Distinct80
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.210807
Minimum0
Maximum450
Zeros15
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2024-12-18T17:12:23.260431image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.99
Q15.64
median9.34
Q323.99
95-th percentile48.99
Maximum450
Range450
Interquartile range (IQR)18.35

Descriptive statistics

Standard deviation23.497427
Coefficient of variation (CV)1.3652717
Kurtosis109.28361
Mean17.210807
Median Absolute Deviation (MAD)7.12
Skewness8.003066
Sum24112.34
Variance552.12909
MonotonicityNot monotonic
2024-12-18T17:12:23.354989image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.93 129
 
9.2%
8.49 127
 
9.1%
3.99 81
 
5.8%
0.99 65
 
4.6%
27.49 64
 
4.6%
23.99 52
 
3.7%
7.25 47
 
3.4%
48.99 45
 
3.2%
6.99 43
 
3.1%
9.34 42
 
3.0%
Other values (70) 706
50.4%
ValueCountFrequency (%)
0 15
 
1.1%
0.5 5
 
0.4%
0.85 6
 
0.4%
0.95 17
 
1.2%
0.99 65
4.6%
1.45 14
 
1.0%
1.5 1
 
0.1%
1.99 36
2.6%
2.49 4
 
0.3%
2.64 3
 
0.2%
ValueCountFrequency (%)
450 1
 
0.1%
259.61 1
 
0.1%
257 2
 
0.1%
162 3
 
0.2%
127 2
 
0.1%
117 8
0.6%
100 1
 
0.1%
67.99 1
 
0.1%
60 1
 
0.1%
57.99 3
 
0.2%

Item Options Total Price
Real number (ℝ)

High correlation  Zeros 

Distinct234
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.80242
Minimum0
Maximum450
Zeros15
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2024-12-18T17:12:23.449011image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.99
Q16.26
median10.94
Q323.99
95-th percentile48.99
Maximum450
Range450
Interquartile range (IQR)17.73

Descriptive statistics

Standard deviation23.593913
Coefficient of variation (CV)1.3253206
Kurtosis107.17806
Mean17.80242
Median Absolute Deviation (MAD)6.95
Skewness7.9013024
Sum24941.19
Variance556.67273
MonotonicityNot monotonic
2024-12-18T17:12:23.540924image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.49 96
 
6.9%
3.99 81
 
5.8%
0.99 65
 
4.6%
34.93 64
 
4.6%
27.49 46
 
3.3%
48.99 45
 
3.2%
9.34 40
 
2.9%
1.99 36
 
2.6%
23.99 33
 
2.4%
4.99 32
 
2.3%
Other values (224) 863
61.6%
ValueCountFrequency (%)
0 15
 
1.1%
0.5 5
 
0.4%
0.85 6
 
0.4%
0.95 17
 
1.2%
0.99 65
4.6%
1.45 14
 
1.0%
1.5 1
 
0.1%
1.99 36
2.6%
2.49 4
 
0.3%
2.64 3
 
0.2%
ValueCountFrequency (%)
450 1
 
0.1%
260 1
 
0.1%
259.61 1
 
0.1%
257 1
 
0.1%
162 3
0.2%
127 2
 
0.1%
120 3
0.2%
117 5
0.4%
100 1
 
0.1%
67.99 1
 
0.1%

Item Total Price
Real number (ℝ)

High correlation  Zeros 

Distinct599
Distinct (%)42.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.940514
Minimum0
Maximum567.98
Zeros15
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2024-12-18T17:12:23.627556image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.08
Q17.38
median13.08
Q327.35
95-th percentile53.65
Maximum567.98
Range567.98
Interquartile range (IQR)19.97

Descriptive statistics

Standard deviation33.87017
Coefficient of variation (CV)1.5437273
Kurtosis101.38988
Mean21.940514
Median Absolute Deviation (MAD)8.71
Skewness8.2777867
Sum30738.66
Variance1147.1884
MonotonicityNot monotonic
2024-12-18T17:12:23.718654image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.37 48
 
3.4%
1.08 36
 
2.6%
9.3 35
 
2.5%
10.23 33
 
2.4%
38.25 31
 
2.2%
26.27 20
 
1.4%
7.94 18
 
1.3%
19.11 17
 
1.2%
25.68 16
 
1.1%
53.64 16
 
1.1%
Other values (589) 1131
80.7%
ValueCountFrequency (%)
0 15
1.1%
0.54 2
 
0.1%
0.55 3
 
0.2%
0.91 2
 
0.1%
0.93 2
 
0.1%
0.97 1
 
0.1%
0.99 2
 
0.1%
1.02 3
 
0.2%
1.03 3
 
0.2%
1.04 7
0.5%
ValueCountFrequency (%)
567.98 1
0.1%
492.75 1
0.1%
438 1
0.1%
284.7 1
0.1%
283.8 1
0.1%
281.41 1
0.1%
267.74 1
0.1%
229.49 1
0.1%
177.39 2
0.1%
176.92 1
0.1%

Interactions

2024-12-18T17:12:16.119307image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:12.119828image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:12.709311image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:13.257486image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:13.804168image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:14.360130image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:15.063708image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:15.578003image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:16.186066image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:12.189689image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:12.780125image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:13.330199image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:13.871957image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:14.429599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:15.128614image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:15.646447image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:16.255084image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:12.264654image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:12.848808image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:13.398907image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:13.940688image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:14.495134image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:15.195204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:15.713986image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:16.330832image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:12.339097image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:12.918435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:13.467232image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:14.000690image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:14.567035image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:15.261555image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:15.786283image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:16.392683image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:12.414623image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:12.986452image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:13.530943image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:14.063938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:14.631215image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:15.323484image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:15.849032image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:16.464387image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:12.489377image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:13.056545image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:13.603315image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:14.128515image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:14.699514image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:15.389586image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:15.920100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:16.527777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:12.560899image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:13.121582image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:13.669322image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:14.191458image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:14.928551image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:15.452203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:15.983000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:16.592993image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:12.637090image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:13.187459image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:13.734752image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:14.290897image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:14.994020image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:15.514026image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-18T17:12:16.049248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2024-12-18T17:12:23.790542image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ChannelsFulfillment TypeItem Options Total PriceItem PriceItem QuantityItem Total PriceItem VariationOrder SubtotalOrder Tax TotalOrder TotalRecipient AddressRecipient Address 2Recipient CityRecipient CountryRecipient Postal Code
Channels1.0000.6510.5010.5010.1570.5260.1490.4290.4290.4341.0001.0001.0000.9991.000
Fulfillment Type0.6511.0000.1120.1120.1560.2270.1180.4730.4730.4750.6800.9300.8250.9970.586
Item Options Total Price0.5010.1121.0000.987-0.1400.9630.0650.0640.0730.0680.4490.7100.1130.1390.007
Item Price0.5010.1120.9871.000-0.1320.9530.0650.0680.0770.0720.4490.7100.1130.1390.026
Item Quantity0.1570.156-0.140-0.1321.0000.0710.0000.2300.2290.2301.0001.0001.0000.2440.018
Item Total Price0.5260.2270.9630.9530.0711.0000.0000.1190.1310.1230.4400.9300.1310.3700.012
Item Variation0.1490.1180.0650.0650.0000.0001.0000.0360.0360.0460.0000.0000.1240.2120.206
Order Subtotal0.4290.4730.0640.0680.2300.1190.0361.0000.9890.9960.7211.0000.1600.828-0.036
Order Tax Total0.4290.4730.0730.0770.2290.1310.0360.9891.0000.9890.7211.0000.1600.828-0.036
Order Total0.4340.4750.0680.0720.2300.1230.0460.9960.9891.0000.5881.0000.1700.827-0.046
Recipient Address1.0000.6800.4490.4491.0000.4400.0000.7210.7210.5881.0001.0000.8001.0000.983
Recipient Address 21.0000.9300.7100.7101.0000.9300.0001.0001.0001.0001.0001.0001.0001.0000.930
Recipient City1.0000.8250.1130.1131.0000.1310.1240.1600.1600.1700.8001.0001.0001.0000.683
Recipient Country0.9990.9970.1390.1390.2440.3700.2120.8280.8280.8271.0001.0001.0001.0001.000
Recipient Postal Code1.0000.5860.0070.0260.0180.0120.206-0.036-0.036-0.0460.9830.9300.6831.0001.000

Missing values

2024-12-18T17:12:16.718002image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-18T17:12:17.007995image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-18T17:12:17.188481image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

OrderOrder DateCurrencyOrder SubtotalOrder Shipping PriceOrder Tax TotalOrder TotalOrder Refunded AmountFulfillment DateFulfillment TypeFulfillment StatusChannelsFulfillment LocationFulfillment NotesRecipient NameRecipient EmailRecipient PhoneRecipient AddressRecipient Address 2Recipient Postal CodeRecipient CityRecipient RegionRecipient CountryItem QuantityItem NameItem SKUItem VariationItem ModifiersItem PriceItem Options Total PriceItem Total Price
0Uber Eats Delivery 8F8192024/12/18USD37.90NaN3.4641.36NaN12/18/2024, 1:58 PMPickupCompletedPostmates DeliveryBelly RubbNaN8F819-Diana O.NaN+1 312-766-6835NaNNaNNaNNaNNaNNaN1CRISPY CHICKEN SANDWICHNaNRegularNaN16.4616.4618.02
1Uber Eats Delivery 8F8192024/12/18USD37.90NaN3.4641.36NaN12/18/2024, 1:58 PMPickupCompletedPostmates DeliveryBelly RubbNaN8F819-Diana O.NaN+1 312-766-6835NaNNaNNaNNaNNaNNaN1LOADED FRIESNaNRegular1 x Signature BBQ Sauce Drizzle, 1 x Salt and Pepper11.9912.7413.95
2Uber Eats Delivery 8F8192024/12/18USD37.90NaN3.4641.36NaN12/18/2024, 1:58 PMPickupCompletedPostmates DeliveryBelly RubbNaN8F819-Diana O.NaN+1 312-766-6835NaNNaNNaNNaNNaNNaN1CREAMY BLUE CHEESE DIPNaNRegularNaN1.451.451.45
3Uber Eats Delivery 8F8192024/12/18USD37.90NaN3.4641.36NaN12/18/2024, 1:58 PMPickupCompletedPostmates DeliveryBelly RubbNaN8F819-Diana O.NaN+1 312-766-6835NaNNaNNaNNaNNaNNaN1ARTISAN MAC AND CHEESENaNSideNaN7.257.257.94
4Uber Eats Delivery 7751A2024/12/17USD113.09NaN10.74123.83NaN12/17/2024, 7:56 PMPickupCompletedPostmates DeliveryBelly RubbNaN7751A-Lilit A.NaN+1 312-766-6835NaNNaNNaNNaNNaNNaN3BEEF SHORT RIBNaNRegularNaN33.9533.95111.52
5Uber Eats Delivery 7751A2024/12/17USD113.09NaN10.74123.83NaN12/17/2024, 7:56 PMPickupCompletedPostmates DeliveryBelly RubbNaN7751A-Lilit A.NaN+1 312-766-6835NaNNaNNaNNaNNaNNaN1ARTISAN MAC AND CHEESENaNSideNaN7.257.257.94
6Uber Eats Delivery 7751A2024/12/17USD113.09NaN10.74123.83NaN12/17/2024, 7:56 PMPickupCompletedPostmates DeliveryBelly RubbNaN7751A-Lilit A.NaN+1 312-766-6835NaNNaNNaNNaNNaNNaN1GRILLED SWEET CORNNaNRegularNaN3.993.994.37
7Postmates Delivery 3BDB72024/12/17USD26.32NaN2.5028.82NaN12/17/2024, 7:41 PMPickupCompletedPostmates DeliveryBelly RubbNaN3BDB7-Arman D.NaN+1 312-766-6835NaNNaNNaNNaNNaNNaN2BELLY BITESNaN4 BitesNaN8.498.4918.59
8Postmates Delivery 3BDB72024/12/17USD26.32NaN2.5028.82NaN12/17/2024, 7:41 PMPickupCompletedPostmates DeliveryBelly RubbNaN3BDB7-Arman D.NaN+1 312-766-6835NaNNaNNaNNaNNaNNaN1CRINKLE FRIESNaNFull1 x Lemon Pepper9.349.3410.23
9DOORDASH2024/12/17USD34.19NaN3.2537.44NaN12/17/2024, 7:27 PMPickupCompletedDoorDashBelly RubbNaNPeter Kpoint-of-sale-integration@doordash.com8552228111NaNNaNNaNNaNNaNNaN1PEAR GORGONZOLA SALADNaNRegularNaN8.498.499.30
OrderOrder DateCurrencyOrder SubtotalOrder Shipping PriceOrder Tax TotalOrder TotalOrder Refunded AmountFulfillment DateFulfillment TypeFulfillment StatusChannelsFulfillment LocationFulfillment NotesRecipient NameRecipient EmailRecipient PhoneRecipient AddressRecipient Address 2Recipient Postal CodeRecipient CityRecipient RegionRecipient CountryItem QuantityItem NameItem SKUItem VariationItem ModifiersItem PriceItem Options Total PriceItem Total Price
1391DOORDASH2024/01/05USD20.81NaN1.9822.79NaN01/05/2024, 1:49 PMPickupCompletedDoorDashBelly RubbNaNTimothy Lpoint-of-sale-integration@doordash.com8559731040NaNNaNNaNNaNNaNNaN1BELLY SLIDERSNaN2 sliders1 x Signature BBQ Sauce9.9810.8311.86
1392DOORDASH2024/01/04USD35.37NaN2.6037.97NaN01/04/2024, 5:31 PMPickupCompletedDoorDashBelly RubbNaNBenjamin Bpoint-of-sale-integration@doordash.com8559731040NaNNaNNaNNaNNaNNaN1GRILLED SWEET CORNNaNRegularNaN2.992.993.21
1393DOORDASH2024/01/04USD35.37NaN2.6037.97NaN01/04/2024, 5:31 PMPickupCompletedDoorDashBelly RubbNaNBenjamin Bpoint-of-sale-integration@doordash.com8559731040NaNNaNNaNNaNNaNNaN1CRINKLE FRIESNaNSide, Rosemary pepper1 x Sweet and Spicy BBQ Sauce, 1 x Signature BBQ Sauce4.996.697.18
1394DOORDASH2024/01/04USD35.37NaN2.6037.97NaN01/04/2024, 5:31 PMPickupCompletedDoorDashBelly RubbNaNBenjamin Bpoint-of-sale-integration@doordash.com8559731040NaNNaNNaNNaNNaNNaN1GET YOUR BABY BACK!NaNHalf rack1 x Sweet and Spicy BBQ Sauce, 1 x Signature BBQ Sauce23.9925.6927.58
1395DOORDASH2024/01/03USD24.84NaN2.3627.20NaN01/03/2024, 2:29 PMPickupCompletedDoorDashBelly RubbNaNAlan Spoint-of-sale-integration@doordash.com8559731040NaNNaNNaNNaNNaNNaN1GET YOUR BABY BACK!NaNHalf rack1 x Signature BBQ Sauce23.9924.8427.20
1396Square Online 8243585682023/12/30USD36.17NaN3.4445.04NaN12/30/2023, 4:15 PMPickupCompletedBELLY RUBB | BBQ Catering | Barbecue To Go and DeliveryBelly RubbNaNNarek Ekmekjyannarek.ek@gmail.com+1310663244713346 Saticoy Stunit 191605.0Los AngelesCAUS1GET YOUR BABY BACK!NaNHalf rack1 x Pickled peppers, 1 x Signature BBQ Sauce, 1 x Sweet and Spicy BBQ Sauce23.9926.1928.68
1397Square Online 8243585682023/12/30USD36.17NaN3.4445.04NaN12/30/2023, 4:15 PMPickupCompletedBELLY RUBB | BBQ Catering | Barbecue To Go and DeliveryBelly RubbNaNNarek Ekmekjyannarek.ek@gmail.com+1310663244713346 Saticoy Stunit 191605.0Los AngelesCAUS1BELLY SLIDERSNaN2 slidersNaN9.989.9810.93
1398DOORDASH2023/12/30USD53.40NaN4.3157.71NaN12/30/2023, 3:50 PMPickupCompletedDoorDashBelly RubbNaNPickUp-Narek Epoint-of-sale-integration@doordash.com8559731040NaNNaNNaNNaNNaNNaN1BELLY SLIDERSNaN4 sliders1 x Sweet and Spicy BBQ Sauce, 1 x Signature BBQ Sauce14.5716.2717.58
1399DOORDASH2023/12/30USD53.40NaN4.3157.71NaN12/30/2023, 3:50 PMPickupCompletedDoorDashBelly RubbNaNPickUp-Narek Epoint-of-sale-integration@doordash.com8559731040NaNNaNNaNNaNNaNNaN1GET YOUR BABY BACK!NaNFull Rack1 x Sweet and Spicy BBQ Sauce, 1 x Signature BBQ Sauce, 1 x Pickled peppers34.9337.1340.13
1400DOORDASH2023/12/28USD9.34NaN0.8910.23NaN12/28/2023, 1:30 PMPickupCompletedDoorDashBelly RubbNaNTest Tpoint-of-sale-integration@doordash.com8559731040NaNNaNNaNNaNNaNNaN1CRINKLE FRIESNaNFull, Truffle salt1 x Blue Cheese8.499.3410.23

Duplicate rows

Most frequently occurring

OrderOrder DateCurrencyOrder SubtotalOrder Tax TotalOrder TotalFulfillment DateFulfillment TypeFulfillment StatusChannelsFulfillment LocationFulfillment NotesRecipient NameRecipient EmailRecipient PhoneRecipient AddressRecipient Address 2Recipient Postal CodeRecipient CityRecipient RegionRecipient CountryItem QuantityItem NameItem VariationItem ModifiersItem PriceItem Options Total PriceItem Total Price# duplicates
0DOORDASH2024/05/18USD76.056.7582.805/18/2024, 9:04 PMPickupCompletedDoorDashBelly RubbNaNDavid Cpoint-of-sale-integration@doordash.com8559731040NaNNaNNaNNaNNaNNaN1BAKED BABY POTATOESRegularNaN6.296.296.852
1DOORDASH2024/05/18USD76.056.7582.805/18/2024, 9:04 PMPickupCompletedDoorDashBelly RubbNaNDavid Cpoint-of-sale-integration@doordash.com8559731040NaNNaNNaNNaNNaNNaN1Beef Short RibRegularNaN27.4927.4929.932